Discrimination Performance Research Articles

Influence of medical conditions on the diagnostic accuracy of plasma p-tau217 and p-tau217/Aβ42.

Blood-based biomarkers (BBMs) can enable early detection of brain amyloid beta (Aβ) pathology in cognitively unimpaired individuals. However, the extent to which common medical conditions affect biomarker performance remains unclear. Participants (n=348) included individuals without cognitive impairment. We studied how brain Aβ associated with BBMs (Aβ42/40, phosphorylated tau [p-tau] 181 and 217, p-tau217/Aβ42, glial fibrillary acidic protein [GFAP], and neurofilament light [NfL]) and optimal BBM thresholds for predicting brain Aβ positivity and whether they are obscured by the presence of common medical conditions. Plasma Aβ42/40, p-tau181, p-tau217, and GFAP, but not NfL, were significantly associated with brain Aβ. P-tau217/Aβ42 showed the best discriminative performance (area under the curve: 0.91). The strength of p-tau217-brain Aβ associations were obscured by diabetes and cardiovascular conditions. These results suggest BBMs may help detect early Aβ pathology but suggest caution in their use due to common medical conditions that could affect accuracy. Plasma Aβ42/40, p-tau181, p-tau217, and GFAP but not NfL showed significant associations with brain Aβ. BBMs were more strongly associated with the level of brain Aβ in those without diabetes and cardiovascular conditions. P-tau217/Aβ42 showed the best performance (AUC=0.91) in discriminating Aβ presence with an optimal cut-off of>1.2, followed by p-tau217 at>0.46 pg/mL, with performance slightly improving when excluding participants with cardiovascular conditions.

Clinical predictive models for recurrence and survival in treated laryngeal and hypopharyngeal cancer: a systematic review and meta-analysis

BackgroundThe limitations of the traditional TNM system have spurred interest in multivariable models for personalized prognostication in laryngeal and hypopharyngeal cancers (LSCC/HPSCC). However, the performance of these models depends on the quality of data and modelling methodology, affecting their potential for clinical adoption. This systematic review and meta-analysis (SR-MA) evaluated clinical predictive models (CPMs) for recurrence and survival in treated LSCC/HPSCC. We assessed models’ characteristics and methodologies, as well as performance, risk of bias (RoB), and applicability.MethodsLiterature searches were conducted in MEDLINE (OVID), Embase (OVID) and IEEE databases from January 2005 to November 2023. The search algorithm used comprehensive text word and index term combinations without language or publication type restrictions. Independent reviewers screened titles and abstracts using a predefined Population, Index, Comparator, Outcomes, Timing and Setting (PICOTS) framework. We included externally validated (EV) multivariable models, with at least one clinical predictor, that provided recurrence or survival predictions. The SR-MA followed PRISMA reporting guidelines, and PROBAST framework for RoB assessment. Model discrimination was assessed using C-index/AUC, and was presented for all models using forest plots. MA was only performed for models that were externally validated in two or more cohorts, using random-effects model. The main outcomes were model discrimination and calibration measures for survival (OS) and/or local recurrence (LR) prediction. All measures and assessments were preplanned prior to data collection.ResultsThe SR-MA identified 11 models, reported in 16 studies. Seven models for OS showed good discrimination on development, with only one excelling (C-index >0.9), and three had weak or poor discrimination. Inclusion of a radiomics score as a model parameter achieved relatively better performance. Most models had poor generalisability, demonstrated by worse discrimination performance on EV, but they still outperformed the TNM system. Only two models met the criteria for MA, with pooled EV AUCs 0.73 (95% CI 0.71-0.76) and 0.67 (95% CI 0.6-0.74). RoB was high for all models, particularly in the analysis domain.ConclusionsThis review highlighted the shortcomings of currently available models, while emphasizing the need for rigorous independent evaluations. Despite the proliferation of models, most exhibited methodological limitations and bias. Currently, no models can confidently be recommended for routine clinical use.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021248762, identifier CRD42021248762.

Systematic review of prognostic models for predicting recurrence and survival in patients with treated oropharyngeal cancer

ObjectivesThis systematic review aims to evaluate externally validated models for individualised prediction of recurrence or survival in adults treated with curative intent for oropharyngeal cancer.DesignSystematic review.SettingHospital care.MethodsSystematic searches were conducted...

Altered platelet phenotype in patients with type II endoleaks following abdominal aortic aneurysm repair.

Endovascular abdominal aortic aneurysm repair (EVAR) is a preferred surgery to prevent aneurysm sac enlargement and minimize the risk of life-threatening rupture in patients with AAA. Serious complications of type II endoleaks following EVAR can cause sac expansion and increase rupture risk. This study focused on evaluating clinical and blood characteristics in patients with type II endoleaks to refine our understanding of systemic fluctuations associated with unsuccessful EVAR. This retrospective study included 146 patients with AAA who underwent primary elective endovascular procedures (EVAR/fEVAR) between 2013 and 2021. Clinical characteristics, complete blood count (CBC) and imaging data were analyzed from patients who did and did not develop type II endoleaks. Mean platelet volume (MPV) was significantly increased in patients who developed type II endoleaks after EVAR. Receiver operating characteristic analysis showed that MPV has a satisfactory discriminatory performance in distinguishing post-EVAR patients who developed type II endoleaks, yielding an area under the curve (AUC) value of 0.64. A risk stratification panel incorporating MPV, type II diabetes history, and administration of dual antiplatelet therapies yielded an AUC of 0.70 and predicted an endoleak-free survival rate with a hazard ratio of 2.94. A nomogram revealed that MPV had the highest scoring weight among all significant variables. Patients with type II endoleaks following EVAR have elevated MPV indicative of different phenotypes of circulating platelets. MPV presents an attractive predictive criteria for assessing the occurrence of type II endoleaks in patients with AAA.

Pattern Separation Involves Regions Beyond the Hippocampus in Non-demented elderly Individuals: a 7T Object Lure Task fMRI Study

Abstract Investigating the mechanism of differentiating similar representations, known as pattern separation, has primarily focused on the hippocampus. The roles of cortical regions and their interaction with the hippocampus remain largely unclear. In this study, we address this topic by analyzing whole-brain, high-resolution mnemonic similarity task (MST) fMRI data collected with a 7-Tesla MR scanner. Structural and functional MRI data were acquired from 55 non-demented elderly subjects. During the encoding phase of the MST task, participants responded with “indoor” or “outdoor” to 66 everyday objects. In the recognition phase, participants were asked to make “same” / “similar” / “new” judgements about objects that were either the same as previously seen objects (targets), similar but different from previously seen objects (lures), or new objects (foils). A general linear model was conducted on hippocampal regions of interest (ROIs) and at the whole-brain level with five conditions, including “new” response to foils (foil), “same” response to targets (hit), correct “similar” response to lure (lure correct rejection, LureCR), false “same” response to lure (lure false alarm, LureFA), and one condition for all others. The activity difference when lures were identified as “similar” compared to “same” (LureCR versus LureFA) was used to assess if a region is involved in pattern separation. An association analysis was conducted to test if lure discrimination performance was correlated with activity difference of hippocampal ROIs between LureCR and LureFA, as well as age. Task-based functional connectivity between hippocampal ROIs and other regions involved in pattern separation was examined. In the hippocampal ROI analysis, left anterior CA3/DG showed greater activity in LureCR condition compared to LureFA. All other hippocampal ROIs did not show differential activity. Better lure discrimination performance was associated with larger activity difference between LureCR and LureFA at left anterior CA3/DG and right anterior subiculum. In the whole-brain analyses, regions in the frontoparietal network (FPN) consistently showed increased activity in the Hit, LureCR and LureFA conditions, and the activity was right-lateralized for Hit and LureFA conditions but bilateral for LureCR condition. Eleven clusters, mainly located in the left hemisphere, were identified to show significant activity difference between LureCR and LureFA condition, including left FPN, middle temporal lobe and subcortical regions. In summary, with the whole-brain high-resolution MST fMRI data, regions exhibiting the pattern separation signature were found to be lateralized to the left hemisphere in elderly participants. The left and right FPN are suggested to have distinct functional roles in the MST. The right FPN contributes to retrieving previously viewed same or similar objects, while the left FPN is preferentially involved in pattern separation. Furthermore, the pattern separation process might require the coordinated effort of FPN and hippocampus, with their interaction potentially mediated by subcortical regions.

Biochemical components of corneal stroma: a study on myopia classification based on Raman spectroscopy and deep learning methods

The study aimed to identify differences in the biochemical composition of corneal stroma lenses across varying degrees of myopia using Raman spectrum characteristics. Corneal stroma lens samples from 38 patients who underwent small incision lens extraction (SMILE) surgery, were categorized into low (n = 9, spherical power ≧ -3.00D), moderate (n = 23, spherical power < -3.00D and > -6.00D), and high myopia (n = 6, spherical power ≦-6.00D) groups. A custom-built microscopic confocal Raman system (MCRS) was used to collect Raman spectra, which were processed by smoothing, denoising, and baseline calibrating to refine raw data. Independent sample t-tests were used to analyze spectral feature peaks among sample types. Significant differences (P < 0.001) were found in multiple Raman spectral characteristic peaks (854 cm-1, 937 cm-1, 1002 cm-1, 1243 cm-1, 1448 cm-1, and 2940 cm-1) between low and high myopia samples, particularly at 2940 cm-1. Differences were also found between low and moderate, and moderate and high myopia samples, although fewer than between low and high myopia samples. The three-classification model, particularly with PLS-KNN training, exhibited superior discriminative performance with accuracy rates of 95%. Similarly, the two-classification model for low and high myopia achieved high accuracy with PLS-KNN (94.4%) compared to PCA-KNN (93.3%). PLS dimensionality reduction slightly outperformed PCA, enhancing classification accuracy. In addition, in both reduction methods, the KNN algorithm demonstrated the highest accuracy and performance. The optimal PLS-KNN classification model showed AUC values of 0.99, 0.98, and 1.00 for ROC curves corresponding to low, moderate, and high myopia, respectively. Classification accuracy rates were 89.7% and 96.9%, and 100% for low and high myopia, respectively. For the two-classification model, accuracy reached 94.4% with an AUC of 0.98, indicating strong performance in distinguishing between high and low myopic corneal stroma. We found significant biochemical differences such as collagen, lipids, and nucleic acids in corneal stroma lenses across varying degrees of myopia, suggesting that Raman spectroscopy holds substantial potential in elucidating the pathogenesis of myopia.

Predictive value of 18F-fluorodeoxyglucose uptake for axillary lymph node metastasis in operable breast cancer: impact of molecular subtypes.

To evaluate the predictive value of standardized uptake value (SUV) in both primary tumors and axillary lymph nodes (ALNs) using FDG PET/CT for lymph node metastasis in breast cancer patients, and to assess the influence of molecular subtypes on this predictive performance. This retrospective study included 287 patients with invasive ductal carcinoma (IDC) who underwent FDG PET/CT prior to surgery between September 2016 and December 2019. The maximum standardized uptake value (SUVmax)of primary tumors (SUV-B) and ALNs (SUV-LN) were analyzed. Molecular subtypes were classified as hormone receptor-positive, HER2-positive, and triple-negative breast cancer (TNBC). Receiver operating characteristic (ROC) curve analysis was performed to assess and compare the diagnostic performance of SUV-B and SUV-LN for predicting ALN metastasis. Among the 287 patients, 62 (21.6%) had confirmed ALN metastasis. The median SUV-LN was significantly higher in patients with metastasis compared to those without metastasis(1.5 vs. 0.9; P < 0.001). SUV-LN demonstrated good discriminative performance for ALN metastasis (AUC: 0.796), whereas SUV-B did not show significant predictive value (AUC: 0.536). The SUV_LN demonstrated significantly lower predictive performance for ALN metastasis in the hormone-positive group (AUC: 0.796) compared to the excellent discriminative performance in the HER2-positive (AUC: 0.923, P = 0.018) and TNBC (AUC: 0.940, P = 0.004) groups. Hormone receptor-positive tumors also exhibited lower FDG uptake in metastatic lymph nodes compared to HER2-positive and TNBC subtypes (P = 0.031). FDG PET/CT SUV-LN effectively predicts ALN metastasis in HER2-positive and TNBC subtypes. Hormone receptor-positive breast cancers show lower FDG uptake in metastatic ALNs, reducing diagnostic accuracy. This finding may aid in selecting the most appropriate diagnostic modality based on tumor characteristics in the era of personalized medicine.

Evaluation of prognostic scores in HCC patients undergoing 1L immunotherapy with atezolizumab and bevacizumab

Background & AimsImmunotherapy with atezolizumab and bevacizumab (a+b) has improved prognosis of patients with unresectable hepatocellular carcinoma (HCC). However, the outcome for individual patients is highly variable. This study aimed to i) develop and validate a prognostic prediction model to estimate individual prognosis and ii) compare it with established models. MethodsIn this multicenter retrospective study, HCC patients undergoing 1L immunotherapy with a+b from 24 centers (Europe, US) were included. Statistical analysis and reporting followed the TRIPOD guidelines. The primary objective was overall survival (OS). A Cox model was developed and externally validated. ResultsIn total, 683 patients were included (training: 526, validation: 157). C-reactive protein, albumin, bilirubin, lymphocytes, ECOG performance status, and extrahepatic spread (CABLE score) remained significantly associated with OS in Cox regression analysis. In the training set, the CABLE score had a higher discriminatory accuracy relative to ALBI, EZ-ALBI, mALBI, CRAFITY, PNI, NLR, PLR, and GPS (time-dependent AUC 0.79 and C-index 0.75 (95% CI 0.71-0.78) at 12 months). In the external validation set, the discriminatory performance of the CABLE score was comparable to ALBI, EZ-ALBI, and mALBI, but on average higher than PNI, CRAFITY, NLR, PLR, and GPS. In patients with Child-Pugh A, the CABLE score outperformed ALBI, EZ-ALBI, and mALBI in the first 9 months. We provide a web-based calculator for the CABLE score to allow estimation of individual prognosis for these patients (http://shiny.imbei.uni-mainz.de:3838/CABLE_Score/). ConclusionsThe CABLE score shows good discriminatory performance in assessing the individual prognosis of patients undergoing 1L immunotherapy with a+b. Further validation studies are needed to investigate its performance compared to the ALBI score, in particular in subgroup analysis. Impact and ImplicationsThe CABLE score allows to estimate prognosis of patients with uHCC undergoing 1L immunotherapy with a+b at an individual level using our web-based calculator. This feature as well as the evaluation of the score’s added benefit through an extensive comparison with other established scores can inform clinicians on their significance and may guide clinical decision-making in the context of a malignant disease whose prognosis has become highly variable. Further large validation studies are needed to investigate the incremental value of the CABLE score compared to the ALBI score, in particular in subgroups such as Child-Pugh A patients.

The effects of acute exercise on emotional pattern separation in adolescents and young adults

Acute exercise has been associated with cognitive improvements, particularly in memory processes linked to the hippocampus, such as the ability to discriminate between similar stimuli, called hippocampal pattern separation. This can be assessed behaviorally with a mnemonic discrimination task and neurally with functional magnetic resonance imaging (fMRI). Additionally, previous research has shown an emotional modulatory effect on pattern separation, involving the amygdala. In this randomized between-subject study, we investigated whether a 10-minute bout of moderate-intensity exercise, compared to rest, could enhance pattern separation of neutral and emotional images in a group of healthy adolescents and young adults (n=53). Our results showed no significant effects of exercise on either mnemonic discrimination performance or neural activity in the hippocampus and amygdala. Additionally, arterial spin labeling (ASL) confirmed that there were no significant differences in cerebral blood flow between exercise and rest. We did observe worse discrimination for images with a higher similarity level and worse discrimination for highly similar positive images compared to negative and neutral images. However, there were no significant correlations between behavioral outcomes and neural activity. Exploratory analysis revealed a neural signal consistent with pattern completion in the bilateral CA1 and left CA3, but no evidence of pattern separation. Future studies should optimize the exercise characteristics necessary to robustly enhance pattern separation.

Development and Validation of Dynamic 5-Year Breast Cancer Risk Model Using Repeated Mammograms.

Current image-based long-term risk prediction models do not fully use previous screening mammogram images. Dynamic prediction models have not been investigated for use in routine care. We analyzed a prospective WashU clinic-based cohort of 10,099 cancer-free women at entry (between November 3, 2008 and February 2012). Follow-up through 2020 identified 478 pathology-confirmed breast cancers (BCs). The cohort included 27% Black women. An external validation cohort (Emory) included 18,360 women screened from 2013, followed through 2020. This included 42% Black women and 332 pathology-confirmed BC excluding those diagnosed within 6 months of screening. We trained a dynamic model using repeated screening mammograms at WashU to predict 5-year risk. This opportunistic screening service presented a range of mammogram images for each woman. We applied the model to the external validation data to evaluate discrimination performance (AUC) and calibrated to US SEER. Using 3 years of previous mammogram images available at the current screening visit, we obtained a 5-year AUC of 0.80 (95% CI, 0.78 to 0.83) in the external validation. This represents a significant improvement over the current visit mammogram AUC 0.74 (95% CI, 0.71 to 0.77; P < .01) in the same women. When calibrated, a risk ratio of 21.1 was observed comparing high (>4%) to very low (<0.3%) 5-year risk. The dynamic model classified 16% of the cohort as high risk among whom 61% of all BCs were diagnosed. The dynamic model performed comparably in Black and White women. Adding previous screening mammogram images improves 5-year BC risk prediction beyond static models. It can identify women at high risk who might benefit from supplemental screening or risk-reduction strategies.

Validation of American College of Radiology Bone Reporting and Data System (Bone-RADS) Version 2023 for diagnosis of malignant tumors of appendicular bone on conventional radiographs

PurposeTo evaluate the diagnostic performance of the American College of Radiology(ACR) Bone Reporting and Data System (Bone-RADS) in diagnosis of malignant tumors of the appendicular bone on conventional radiographs. MethodsPrimary and secondary tumors of appendicular bone in patients who underwent radiographic and MRI examinations were classified into benign, intermediate, and malignant using a reference standard of histopathology, imaging follow-up, or clinical-radiologic consensus. Two radiologists assessed five radiographic features (margin, periosteal reaction, endosteal erosion, pathologic fracture, and extra-osseous mass), scored point total (points from radiographic features and a history of cancer), and assigned Bone-RADS categories. The diagnostic performance of Bone-RADS and interreader agreements were calculated. ResultsA total of 778 patients (507 benign, 45 intermediate, and 226 malignant tumors) were included. Bone-RADS showed high discrimination performance, with areas under the receiver-operating characteristics curve of 0.940–0.957 for point total and 0.895–0.900 for categorization. Bone-RADS had high sensitivity (95.2 %–99.1 %) and negative predictive value (NPV) (96.4 %–99.5 %), but relatively low specificity (65.0 %–68.6 %) and positive predictive value (PPV) (53.7 %–61.9 %). Interreader agreements were good to excellent for Bone-RADS point total (ICC = 0.850), categorization (k = 0.739), and most of the radiographic features (k = 0.621–0.822), except for endosteal erosion (k = 0.537) and extra-osseous mass (k = 0.234). ConclusionIn diagnosis of malignant bone tumors, ACR Bone-RADS showed high discrimination performance, with high sensitivity and NPV, but relatively low specificity and PPV. Nevertheless, relatively low interobserver agreement in some radiographic features and the consensus-based points system in Bone-RADS warrant further research and possible updates.

A Parameter Estimation-Based Anti-Deception Jamming Method for RIS-Aided Single-Station Radar

Multi-station radar can provide better performance against deception jamming, but the harsh detection requirements and risk of network destruction undermine the practicability of the multi-station radar. Therefore, it is necessary to further explore the anti-deception jamming performance of a single-station radar. This paper introduces a novel method, based on parameter estimation with a virtual multi-station system, to discriminate range deceptive jamming. The system consists of a single-station radar assisted by the reconfigurable intelligent surfaces (RIS). A unified parameter estimation model for true and false targets is established, and the convex optimization method is applied to estimate the target location and deception range. The Cramer–Rao lower bound (CRLB) of the target localization and the measured deception range is then derived. By using the measured deception range and its CRLB, an optimal discrimination algorithm in accordance with the Neyman–Pearson lemma is designed. Simulation results demonstrate the feasibility of the proposed method and analyze the effects of factors such as signal-to-noise ratio (SNR), deception range, jammer location, and the RISs station arrangement on the discrimination performance.

Effective real-time transmission estimations incorporating population viral load distributions amid SARS-CoV-2 variants and pre-existing immunity.

Population-level viral load distribution, measured by cycle threshold (Ct), has been demonstrated to enable real-time estimation of Rt for SARS-CoV-2 ancestral strain. The generalizability of the framework under different circulating variants and pre-existing immunity remains unclear. We obtained the first Ct record of local COVID-19 cases from July 2020 to January 2023 in Hong Kong. The log-linear regression model, fitting on daily Ct mean and skewness to the Rt estimated by case count, was trained using data from wave 3 (i.e., ancestral strain with minimal population immunity), and we predicted Rt for wave 5, 6 and 7 (i.e., Omicron subvariants with > 70% vaccine coverage). Cross-validation was performed by training on the other 4 waves. Stratification analysis by disease severity was conducted to retrospectively evaluate the impact of the changing severity profiles. Trained with the ancestral dominated wave 3, our model can accurately estimate whether Rt was above 1, with the area under the receiver operating characteristic curve of 0.98 (95% confidence interval: 0.96, 1.00), 0.62 (95% CI: 0.53, 0.70) and 0.80 (95% CI: 0.73, 0.88) for three Omicron dominated waves 5 to 7, respectively. Models trained on the other four waves also had discriminative performance. Stratification analysis suggested the potential impact of case severity on model estimation, which coincided with the fluctuation of sampling delay. Our findings suggested that incorporating population viral shedding can provide timely and accurate transmission estimation with evolving variants and population immunity. Model application needs to account for sampling delay.

Mixed style network based: A novel rotating machinery fault diagnosis method through batch spectral penalization

The unsupervised fault diagnosis of rotating machinery holds significant importance, but it still faces numerous complex challenges. For instance, traditional convolutional neural networks often overlook inter-channel relationships, resulting in poor generalization and requiring manual adjustment of architecture parameters for different tasks. Additionally, traditional domain adversarial transfer learning has insufficient research on feature discriminability, leading to less distinguishable features. To address these issues, this paper proposes a MixStyle network based on the SE attention mechanism. This method achieves dynamic weight allocation through the SE attention mechanism, which is simple in design and introduces few additional parameters. By employing the MixStyle method for probabilistic mixed-domain training, the diversity of the source domain is increased, thereby improving the model's generalization capability. Since the principal singular vector enhances feature transferability, this paper penalizes the largest singular value through Batch Spectral Penalization to enhance other feature vectors, improving feature discriminability and domain adversarial performance. Experimental results show that the proposed method demonstrates outstanding performance in the task of unsupervised fault diagnosis for rotating machinery.

Perception of European Portuguese Mid-Vowels by Ukrainian–Russian Bilinguals

Mid-vowel contrasts often present perceptual challenges for speakers of languages that lack these distinctions. However, bilingual speakers, who have access to two phonological systems and exhibit greater metalinguistic awareness, might not necessarily encounter such difficulties. In this study, 27 Ukrainian–Russian bilinguals listened to an unfamiliar language, European Portuguese, and completed two tasks: an identification task where they assimilated the seven stressed oral Portuguese vowels to the closest Ukrainian categories and a discrimination task featuring the Portuguese vowel contrasts /ɛ/–/e/, /e/–/i/, /ɔ/–/o/, and /o/–/u/. No bilingual advantage was observed: the discrimination performance on all contrasts was slightly above or near a chance level (A-prime scores varied between 0.55 and 0.20). These perceptual difficulties may be attributed to the acoustic similarities between the vowels within the contrasts rather than to the differences between the phonological inventories of the languages (the most challenging contrast was not a mid-vowel contrast but acoustically similar /o/–/u/). Although with the back mid-vowel contrast, the difficulty seems to also stem from the possibility that both Ukrainian and Russian have only one back mid-vowel, /o/, and this category occupies a wider area in the vowel space of Ukrainian–Russian bilinguals. The results suggest that bilingual advantage does not always manifest itself in the perception of a new language, especially if two typologically close languages are involved.

The NACA score predicts mortality in polytrauma patients before hospital admission: a registry-based study

BackgroundThe early assessment of the severity of polytrauma patients is key for their optimal management. The aim of this study was to investigate the discriminative performance of the NACA score in a large dataset by stratifying the severity of polytraumatized patients in correlation to injury severity score (ISS), Glasgow Coma Scale (GCS), and mortality.MethodsThis study on the Swiss Trauma Registry investigated 2239 polytraumatized patient (54.3 ± 22.8 years) enrolled from 2015 to 2023: 0.5% were NACA 3, 76.7% NACA 4, 21.4% NACA 5, and 1.4% NACA 6. The NACA predictive value of patients’ mortality was investigated, as well as the correlation of ISS and GCS scores, and other factors influencing patients’ survival at discharge and after 28 days.ResultsIn NACA 4 and 5 the survival rate during hospitalization was 97.7% and 82.5%, respectively, and 28-day mortality 3.5% and 23.5%, respectively (p < 0.0005). NACA correlated with GCS in the prehospital phase and in the emergency room (p < 0.0005), as well as with ISS (p < 0.0005). NACA 4 and 5 presented different injury patterns (fall < 3 m vs vehicle accident) with NACA 5 requiring more CPR and intubation (p < 0.001, p < 0.0005). The ROC AUC analysis showed the prehospital NACA and GCS values as the strongest variables predicting patients’ survival.ConclusionsThis study provides valuable evidence supporting the effectiveness of the NACA score in assessing the severity of polytrauma patients in both the pre-ER and ER condition. Considering the statistical significant correlation with the GCS and with the ISS, NACA is a valid score for assessing polytrauma patients.

Discrimination of wheat gluten quality utilizing terahertz time-domain spectroscopy (THz-TDS)

Wheat is an important food crop in the world, and wheat gluten quality is one of the important standards for judging the use of wheat. In this study, a combination of chemometric and machine learning methods based on THz-TDS were used to identify three different gluten wheats (high gluten, medium gluten, and low gluten). After collecting the time-domain spectral information of the samples, the frequency-domain spectra, refractive index spectra and absorption coefficient spectra of the samples were obtained by calculating the optical parameters. The experimental results showed that there were differences in the refractive indices and absorption coefficients of wheat with different gluten levels. More importantly the differences in refractive index spectra were more significant. The Competitive Adaptive Reweighted Sampling (CARS) method was applied to select characteristic frequencies from the refractive index spectra within the frequency range of 0.1 to 1.5 THz, to establish a discrimination model for wheat gluten strength. We analysed and compared four discriminative models of Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), Improved Convolutional Neural Networks (Improved CNN) and Sparrow Algorithm Optimised Support Vector Machines (SSA-SVM). The final results indicated that the SSA-SVM model demonstrated the optimal discrimination performance, achieving an accuracy rate of 100% as reflected in the confusion matrix. In summary, this study provides an efficient, accurate, and non-destructive discrimination method for wheat gluten strength, offering a theoretical basis for differentiating wheat with varying gluten strengths in production processes. It holds practical significance for industrial production reference.

Development and validation of a risk score for detecting non-alcoholic fatty liver disease.

The development of an easy-to-use noninvasive model to screen nonalcoholic fatty liver disease (NAFLD) is warranted. This study aimed to develop and validate a simple noninvasive NAFLD risk score (NARS). We used the National Health and Nutrition Examination Survey 2017 to March 2020 cycle data. The sample size of derivation and validation cohort were 4056 and 2502, separately. The NAFLD was determined by FibroScan® measured controlled attenuation parameter scores of >285 dB/m in the absence of excessive alcohol use, steatogenic medications use, and viral hepatitis. The NARS was derived from a multivariable logistic regression model and variables were selected based on Boruta analysis. The performance of NARS was internally validated and compared with previous models using receiver-operating characteristics curve and C-statistics. The NARS was established using waist circumference, triglycerides, alanine aminotransferase, and fasting glucose, and the total score ranges from 0 to 8, with an increasing risk of NAFLD. NARS demonstrated ideal discrimination in the validation cohort, with C-statistics of 0.832 (95% confidence interval, 0.801-0.824), and was not inferior to any existing models. The optimal cutoff point for predicting NAFLD was obtained at 4 scores with a sensitivity of 82% and specificity of 69%. We reported the derivation and internal validation of a novel and easy-to-use risk score for detecting the presence of NAFLD. NARS demonstrated ideal discrimination performance and was practical in clinical practice for selecting individuals at higher risk of NAFLD for further examination or intervention.

Comparing conventional and action video game training in visual perceptual learning

Action video game (AVG) playing has been found to transfer to a variety of laboratory tasks in visual cognition. More recently, it has even been found to transfer to low-level visual "psychophysics tasks. This is unexpected since such low-level tasks have traditionally been found to be largely “immune” to transfer from another task, or even from the same task but a different stimulus attribute, e.g., motion direction. In this study, we set out to directly quantify transfer efficiency from AVG training to motion discrimination. Participants (n = 65) trained for 20 h on either a first-person active shooting video game, or a motion direction discrimination task with random dots. They were tested before, midway, and after training with the same motion task and an orientation discrimination task that had been shown to receive transfer from AVG training, but not from motion training. A subsequent control group (n = 18) was recruited to rule out any test–retest effect, by taking the same tests with the same time intervals, but without training. We found that improvement in motion discrimination performance was comparable between the AVG training and control groups, and less than the motion discrimination training group. We could not replicate the AVG transfer to orientation discrimination, but this was likely due to the fact that our participants were practically at chance for this task at all test points. Our study found no evidence, in either accuracy or reaction time, that AVG training transferred to motion discrimination. Overall, our results suggest that AVG training transferred little to lower-level visual skills, refining understanding of the mechanisms by which AVGs may affect vision.Protocol registration The accepted stage 1 protocol for this study can be found on the Open Science Framework at https://osf.io/zdv9c/?view_only=5b3b0c161dad448d9d1d8b14ce91ab11.The stage 1 protocol for this Registered Report was accepted in principle on 01/12/22. The protocol, as accepted by the journal, can be found at: https://doi.org/10.17605/OSF.IO/ZDV9C

External validation of a modified cardiovascular sequential organ failure assessment score in patients with suspected infection using the MIMIC-IV database.

We developed a modified cardiovascular (CV) Sequential Organ Failure Assessment (SOFA) score using an emergency department-based cohort data, incorporating norepinephrine equivalent dose and lactate to represent current clinical practice patterns for vasopressor utilization and the diagnostic significance of lactate, respectively. In this study, we sought to validate this modified CV-SOFA score in intensive care unit patients with suspected infection using the Marketplace for Medical Information in Intensive Care (MIMIC)-IV database. This was a retrospective study that utilized data from the MIMIC-IV database. Modified CV/total SOFA score and original CV/total SOFA score were compared for predicting in-hospital mortality. Area under the receiver operating characteristic curve (AUROC) and the calibration curve were employed to evaluate discrimination and calibration, respectively. A total of 29,618 ICU patients with suspected infections was analyzed. The in-hospital mortality rate was 12.4% (n = 3,675). Modified CV-SOFA score (AUROC 0.667; 95% confidence interval [CI] 0.657-0.677 vs. 0.663; 95% CI 0.654-0.673; p = 0.283) and modified total SOFA score (0.784 [95% CI 0.776-0.793] vs. 0.785 [95% CI 0.777-0.793], p = 0.490) did not differ significantly from the original CV-SOFA score and original total SOFA score, respectively. The calibration curve of the original CV-SOFA score was inferior to that of the modified CV-SOFA score. The modified CV- and total SOFA scores were better calibrated than the original CV- and total SOFA scores, but their discriminative performance was not significantly different. Further studies of the modified CV-SOFA score in different settings and populations are required to assess the generalizability of this score.